This revised textbook motivates and illustrates the techniques of applied probability by
applications in electrical engineering and computer science (EECS). The author presents
information processing and communication systems that use algorithms based on probabilistic
models and techniques including web searches digital links speech recognition GPS route
planning recommendation systems classification and estimation. He then explains how these
applications work and along the way provides the readers with the understanding of the key
concepts and methods of applied probability. Python labs enable the readers to experiment and
consolidate their understanding. The book includes homework solutions and Jupyter notebooks.
This edition includes new topics such as Boosting Multi-armed bandits statistical tests
social networks queuing networks and neural networks. For ancillaries related to this book
including examples of Python demos and also Python labs used in Berkeley please email Mary
James at mary.james@springer.com. This is an open access book.
